Numerical study on heat transfer between airflow and surrounding rock with two major ventilation models in deep coal mine

Abstract

Not only is the thermal environment of the coal mining face related to the temperature of the surrounding rock, it is also closely associated with the ventilation model of the working face. In this study, the numerical methods were applied to study the impact of two major ventilation systems on the airflow temperature of working face in coalmine. Firstly, a heat transfer model of the surrounding rock and airflow was established to reveal that the wall roughness of the surrounding rock could enhance heat transfer between the surrounding rock and the airflow. Moreover, an analysis was conducted of the heat transfer between the airflow and the surrounding rock under different modes of ventilation in the first mining face. According to the analytical results, the temperature of airflow in the U-type ventilation system is lower than in the Y-type ventilation system. For the next adjacent coal mining face, however, the Y-type ventilation system is more conducive in reducing the temperature of the airflow. Therefore, with regard to the mine as a whole, the Y-type ventilation system is more effective than a U-type system in reducing heat and humidity in the ambient environment.

A Study on the Conditions of Fog Generation and the Change Rule of Fog Zone Length in Air Intake Roadway

“…Dechao Deng [30] used the empirical formula of Sherbani and the Lewis relation to determine heat and mass transfer coefficients and established a mathematical model of heat and moisture exchange between the air flow and the wall of the excavation face. Pingye Guo et al [31] discovered that the roughness of the surrounding rock increases the effective heat transfer coefficient between the surrounding rock and the air flow, thereby enhancing the heat transfer between them. These studies have focused on the effects of mine ventilation and air flow temperature and humidity on each other; however, there are no studies that show how these factors affect fog formation.…”

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

“…At present, there are still many difficulties in the accurate perception of the mining environment of the coal mine working face. It is an urgent need to find out an effective and reliable perception means that can adapt to the special mining process in the coal mine working face. − Laser has characteristics of strong dust penetration, good unidirectionality, and strong anti-interference ability. , Therefore, we collect the point cloud of the whole coal mine working face by LiDAR to characterize the mining environment of the working face. Furthermore, the expression of point cloud is simpler.…”